https://github.com/cran/spatstat
Tip revision: 1a4b153dab7a7656ab810d53e156a13a67e4eea5 authored by Adrian Baddeley on 01 August 2018, 14:50:03 UTC
version 1.56-1
version 1.56-1
Tip revision: 1a4b153
summary.kppm.Rd
\name{summary.kppm}
\alias{summary.kppm}
\alias{print.summary.kppm}
\title{Summarizing a Fitted Cox or Cluster Point Process Model}
\description{
\code{summary} method for class \code{"kppm"}.
}
\usage{
\method{summary}{kppm}(object, \dots, quick=FALSE)
\method{print}{summary.kppm}(x, \dots)
}
\arguments{
\item{object}{
A fitted Cox or cluster point process model (object of
class \code{"kppm"}).
}
\item{quick}{Logical value controlling the scope of the summary.}
\item{\dots}{Arguments passed to \code{\link{summary.ppm}} or
\code{\link{print.summary.ppm}} controlling the treatment of
the trend component of the model.}
\item{x}{Object of class \code{"summary.kppm"} as returned by
\code{summary.kppm}.
}
}
\details{
This is a method for the generic \code{\link{summary}}
for the class \code{"kppm"}. An object of class \code{"kppm"}
describes a fitted Cox or cluster point process model.
See \code{\link{kppm}}.
\code{summary.kppm} extracts information about the
type of model that has been fitted, the data to which the model was
fitted, and the values of the fitted coefficients.
\code{print.summary.kppm} prints this information in a
comprehensible format.
In normal usage, \code{print.summary.kppm} is invoked implicitly
when the user calls \code{summary.kppm} without assigning its value
to anything. See the examples.
You can also type \code{coef(summary(object))} to extract a table
of the fitted coefficients of the point process model \code{object}
together with standard errors and confidence limits.
}
\value{
\code{summary.kppm} returns an object of class \code{"summary.kppm"},
while \code{print.summary.kppm} returns \code{NULL}.
The result of \code{summary.kppm} includes at least the
following components:
\item{Xname}{character string name of the original point pattern data}
\item{stationary}{logical value indicating whether the model is
stationary}
\item{clusters}{the \code{clusters} argument to \code{\link{kppm}}}
\item{modelname}{character string describing the model}
\item{isPCP}{\code{TRUE} if the model is a Poisson cluster process,
\code{FALSE} if it is a log-Gaussian Cox process}
\item{lambda}{Estimated intensity: numeric value, or pixel image}
\item{mu}{Mean cluster size: numeric value, pixel image, or
\code{NULL}}
\item{clustpar}{list of fitted parameters for the cluster model}
\item{clustargs}{list of fixed parameters for the cluster model, if
any}
\item{callstring}{character string representing the original call to
\code{\link{kppm}}}
}
\examples{
fit <- kppm(redwood ~ 1, "Thomas")
summary(fit)
coef(summary(fit))
}
\author{
\spatstatAuthors
}
\keyword{spatial}
\keyword{methods}
\keyword{models}